Data-themed articles, essays, and studies

It’s Feasible After All

Congratulations to the Cleveland Cavaliers – 2016 NBA Champions. Many reasonable, rational people believed that Cleveland teams could never prevail in a championship series, due to the unseen but inevitable dynamics of hex, curse, and natural law. But it’s feasible after all.

I often see data and analytics teams labor to attempt the truly infeasible. Or to declare something infeasible when it was entirely possible, or even straightforward.

To me, attempting the infeasible is understandable – teams may not have the tools and techniques to determine if a goal or task can actually be accomplished. Nonetheless, we can and do spend unnecessary time and effort asking the unanswerable, validating beyond our data quality, and predicting the unactionable.

When I’m told something is not feasible, that’s often a euphemism for “risky.” I’m less sanguine about that. Answering worthwhile questions with data is research, and there is no research – or its potential rewards – without risk. My graduate advisor John Dahler would remind me “If we knew exactly what to do, it wouldn’t be research in the first place.” The best Q&A – and its concomitant IT support – involves some creativity and risk. We don’t need to jump off the Eiger in a Wingsuit, just be willing to try new things occasionally, and accept that some of those won’t work out. On the other hand, if our expensive data apparatus serves only to validate what we already know, what is the point?

When I talk about Q&A contexts, risk and feasibility are right in the picture. I do ask that we assume a little risk – in what for many of us will be a slightly different mindset and process. When as a result we focus on what is genuinely relevant and feasible, we’ll save ourselves time and trouble. And when we achieve transparency – each of us with enough knowledge of the entire Q&A process to perform our tasks optimally – we’ll get better outcomes and manage our risk too.